An enormous amount of information now resides in data processing systems around the world. A single large business organization, for example, typically retains millions of documents in various computer systems. In recent years, data mining systems have been developed to help extract kernels of knowledge from large collections of data. Many of those data mining systems provide important advantages in the field of data analysis. Some data mining systems also provide tools for presenting the results of statistical analyses.
For example, one well-known format that may be used to present statistical results is the pie chart. In a conventional pie chart, wedges are presented within a circle or “pie” to represent measures associated with different categories. For instance, a pie chart for government spending includes wedges for different governmental programs, such as military, social security, etc. The wedge for each program occupies a portion of the pie that corresponds to the amount spent by that program, relative to the total amount spent. Thus, a pie chart depicts statistical results involving two parameters, the category parameter and the measure parameter. In the given example, the values for the category parameter are military, social security, etc., and the values for the measure parameter are the amounts spent in each category. In the given example, the wedges thus depict a categorization of spending according to program.
Pie charts are effective for communicating statistical results that are limited to two parameters. However, standard pie charts are not designed to present results that include different values for three or more parameters. For instance, with reference to the above example, if a user is interested in understanding how government spending has changed over time, it is necessary to add a third parameter to the analysis: a time parameter. Furthermore, since a standard pie chart accommodates only two parameters, a separate pie chart must be used for each different year in the analysis. A presentation with a number of pie charts can reveal relatively large changes over time, which show up as noticeable differences between the charts. More subtle changes, however, are not readily apparent. Moreover, as the number of charts to be presented within a given presentation space increases, the size of each chart must decrease, which further reduces the ability to convey any but the most obvious of changes. Furthermore, statistical analyses frequently involve more than three parameters. For example, data mining systems may generate statistics containing summary information for many different parameters or dimensions.
In addition, much of the information that is currently stored in data processing systems relates to information that has traveled from one place or person to another place or person. When studying such information, users may gain valuable insights by considering the flow patterns for the information. For example, it is useful to know which people or departments in an organization have been communicating with which other departments or people. It is also useful to compare different parts of an organization with respect to information flow. Yet additional insight could be obtained through consideration of message content, along with statistics regarding message flow. However, such statistics are difficult to assimilate through direct study, and conventional data presentation facilities lack effective means for presenting summaries of such data.